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A training algorithm for optimum margin classifiers
Proceedings of the Fifth Annual Workshop on Computational Learning Theory 5: 144-152, 1992.
On the subjective meaning of probability
de Finetti Bruno, (1992 [1931]), "On the Subjective Meaning of Probability," in Paola Monari & Daniela Cocchi (eds), Bruno de Finetti: Probabilità e induzione (Induction and Probability), Bologna, CLUEB, 298-329. Title Link: Maria Carla Galavotti. "Pragmatism and the Birth of Subjective Probability". European Journal of Pragmatism and American Philosophy [Online], XI-1 | 2019, Online since 19 July 2019, connection on 21 July 2019.
Complexity results for serial decomposability
Chalasani et al. show that this problem is Korf (1985) presents a method for learning macrooperators in NP, but NPcompleteness is open. Tadepalli (1991a, and shows that the method is applicable 1991b) shows how macro tables are polynomially PAClearnable to serially decomposable problems.
Associative Memory in a Network of `Biological' Neurons
The Hopfield network (Hopfield, 1982,1984) provides a simple model of an associative memory in a neuronal structure. This model, however, is based on highly artificial assumptions, especially the use of formal-two state neurons (Hopfield,1982) or graded-response neurons (Hopfield, 1984).
CAM Storage of Analog Patterns and Continuous Sequences with 3N2 Weights
Box 808 (L-426), Livermore, Ca. 94550 A simple architecture and algorithm for analytically guaranteed associative memorystorage of analog patterns, continuous sequences, and chaotic attractors in the same network is described. A matrix inversion determines network weights, given prototype patterns to be stored.
Dynamics of Learning in Recurrent Feature-Discovery Networks
The self-organization of recurrent feature-discovery networks is studied from the perspective of dynamical systems. Bifurcation theory reveals parameter regimesin which multiple equilibria or limit cycles coexist with the equilibrium at which the networks perform principal component analysis.
Phase-coupling in Two-Dimensional Networks of Interacting Oscillators
Niebur, Ernst, Kammen, Daniel M., Koch, Christof, Ruderman, Daniel L., Schuster, Heinz G.
Coherent oscillatory activity in large networks of biological or artificial neuralunits may be a useful mechanism for coding information pertaining to a single perceptual object or for detailing regularities within a data set. We consider the dynamics of a large array of simple coupled oscillators under a variety of connection schemes. Of particular interest is the rapid and robust phase-locking that results from a "sparse" scheme where each oscillator is strongly coupled to a tiny, randomly selected, subset of its neighbors.